The field of edge computing and microservice architecture is rapidly evolving, with a focus on improving resource utilization, scalability, and real-time processing. Researchers are exploring novel approaches to auto-scaling, task offloading, and digital twin technology to address the challenges of distributed systems. Notable advancements include the development of more efficient and adaptive scaling algorithms, such as those utilizing reinforcement learning and multi-dimensional autoscaling. Additionally, there is a growing interest in optimizing resource allocation and utilization in edge computing environments, with proposals for hybrid frameworks and auto-benchmarking tools. These innovations have the potential to significantly enhance the performance, reliability, and security of edge computing systems and microservice architectures. Noteworthy papers include: SecureSmart HPA, which proposes a resilient and resource-efficient auto-scaling approach for microservice architectures, achieving up to 57.2% reduction in CPU overutilization. MOSE introduces a novel orchestration framework for stateful microservice migration at the edge, resulting in up to 77% decrease in migration downtime. ScalableHD presents a scalable and high-throughput hyperdimensional computing inference framework for multi-core CPUs, achieving up to 10x speedup in throughput over state-of-the-art baselines.